Reliability-constrained Based Optimal Placement and Sizing of Multiple Distributed Generators in Power Distribution Network Using Cat Swarm Optimization

2014 ◽  
Vol 42 (2) ◽  
pp. 149-164 ◽  
Author(s):  
Deepak Kumar ◽  
S. R. Samantaray ◽  
I. Kamwa ◽  
N. C. Sahoo
Keyword(s):  
Power Distribution ◽  
Distribution Network ◽  
Optimal Placement ◽  
Power Distribution Network ◽  
Swarm Optimization ◽  
Cat Swarm Optimization ◽  
Distributed Generators

scholarly journals Multi-Objective Power Distribution Network Reconfiguration using Chaotic Fractional Particle Swarm Optimization

2021 ◽  
Vol 19 (1) ◽  
pp. 43-50
Author(s):  
Yashar Mousavi ◽  
Mohammad Hosein Atazadegan ◽  
Arash Mousavi
Keyword(s):  
Particle Swarm Optimization ◽  
Power Distribution ◽  
Distribution Network ◽  
Particle Swarm ◽  
Superior Performance ◽  
Network Reconfiguration ◽  
Power Distribution Network ◽  
Swarm Optimization ◽  
Distribution Network Reconfiguration ◽  
Multi Objective

Optimization of power distribution system reconfiguration is addressed as a multi-objective problem, which considers the system losses along with other objectives, and provides a viable solution for improvement of technical and economic aspects of distribution systems. A multi-objective chaotic fractional particle swarm optimization customized for power distribution network reconfiguration has been applied to reduce active power loss, improve the voltage profile, and increase the load balance in the system through deterministic and stochastic structures. In order to consider the prediction error of active and reactive loads in the network, it is assumed that the load behaviour follows the normal distribution function. An attempt is made to consider the load forecasting error on the network to reach the optimal point for the network in accordance with the reality. The efficiency and feasibility of the proposed method is studied through standard IEEE 33-bus and 69-bus systems. In comparison with other methods, the proposed method demonstrated superior performance by reducing the voltage deviation and power losses. It also achieved better load balancing.

Power Distribution Network Reconfiguration by Using EPSO for Loss Minimizing

2014 ◽  
Vol 699 ◽  
pp. 809-815 ◽  
Author(s):  
Mohamad Fani Sulaima ◽  
Mohd Hafiz Jali ◽  
Wan Mohd Bukhari ◽  
M.N.M. Nasir ◽  
Hazriq Izzuan Jaafar
Keyword(s):  
Particle Swarm Optimization ◽  
Power Distribution ◽  
Distribution Network ◽  
Heuristic Method ◽  
Particle Swarm ◽  
Computational Time ◽  
Network Reconfiguration ◽  
Voltage Profile ◽  
Power Distribution Network ◽  
Swarm Optimization

Due to the complexity of modern power distribution network, a hybridization of heuristic method which is called as Evolutionary Particle Swarm Optimization (EPSO) is introduced to identify the open and closed switching operation plans for network reconfiguration. The objectives of this work are to reduce the power losses and improve the voltage profile in the overall system meanwhile minimizing the computational time. The proposed combination of Particle Swarm Optimization (PSO) and Evolutionary Programming (EP) is introduced to make it faster in order to find the optimal solution. The proposed method is applied and it impacts to the network reconfiguration for real power loss and voltage profiles is investigated respectively. The proposed method is tested on a IEEE 33-bus system and it is compared to the traditional PSO and EP method accordingly. The results of this study is hoped to help the power engineer to configure the smart and less lossed network in the future.

A Stochastic Model Based on Markov Chain to Support Vehicle-to-Grid (V2G) Operation in Smart Distribution Network

2019 ◽  
Vol 20 (3) ◽  
Author(s):  
Santoshkumar Hampannavar ◽  
Udaykumar R. Yaragatti ◽  
Suresh Chavhan
Keyword(s):  
Power Distribution ◽  
Distribution Systems ◽  
Distribution Network ◽  
Optimal Placement ◽  
Voltage Profile ◽  
Power Losses ◽  
Power Distribution Network ◽  
Vehicle To Grid ◽  
Communication Framework ◽  
Smart Distribution Network

Abstract In this paper a multiagent based communication framework for gridable electric vehicle (GEV) aggregation in power distribution network is proposed. Also, multi objective optimization is presented for the minimization of power losses and maximization of voltage. Furthermore multiagent system (MAS) based analytical model is proposed for GEV aggregation. Comprehensive case studies are conducted on IEEE 33 and 69 bus test distribution systems using MATLAB and it is observed that the timely and optimal placement of GEV aggregation in distribution network using multiagent communication (MAC) will lead to reduction in power losses and improvement in voltage profile. MATLAB and MOBILE C were used for the simulation studies and results demonstrate significant benefits of GEV aggregation in distribution network.

Gridable Electric Vehicle (GEV) Aggregation in Distribution Network to Support Grid Requirements: A Communication Approach

2017 ◽  
Vol 18 (3) ◽  
Author(s):  
Santoshkumar Hampannavar ◽  
Suresh Chavhan ◽  
Udaykumar Yaragatti ◽  
Anant Naik
Keyword(s):  
Energy Storage ◽  
Power Distribution ◽  
Signal To Noise Ratio ◽  
Past History ◽  
Storage System ◽  
Distribution Network ◽  
Energy Storage System ◽  
Load Flow ◽  
Optimal Placement ◽  
Power Distribution Network

Abstract Electric Vehicles (EV) can be connected to the grid for power transaction and also serve as distributed resource (DR) or distributed energy storage system (DESS). The concept of connecting group of EVs or gridable EVs (GEV) to the grid is called Vehicle-to-Grid (V2G). V2G is a prominent energy storage system as it is flexible and can be used to support the grid requirements in order to meet the time varying load demand. Optimal placement of GEV aggregation in power distribution network is very challenging and helps in maintaining stability of the power system for a shorter duration of time. In this paper, algorithm is developed for estimating parameters like Ploss, Qloss, Vpu based on past history and wireless access support for Control and Monitoring Unit (CMU) to aggregator agent communication is proposed using Long Term Evolution (LTE) protocol. The load flow studies are carried using MiPOWER software in order to obtain the optimal location for the placement of GEV aggregation in power distribution network. LTE physical layer is modeled using MATLAB/SIMULINK and the performance is analyzed using bit error rate (BER) v/s signal to noise ratio (SNR) curves.

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